Optimization of DL Technology for Auxiliary Painting System Construction Based on FST Algorithm

The continuous development of computers has brought about the emergence of many image processing software, but these software have relatively limited functions and cannot learn and create works according to the prescribed style. To make it easier for ordinary people to create artistic style paintings, this study proposes the construction of an auxiliary painting system based on finite state transducer algorithm-optimized deep learning technology. The results demonstrated that when there were 12 images, the accuracy of the optimized convolutional neural network model in extracting image features increased by 1.1% compared to before optimization. When the number of images was 1, the optimized model reduced the image feature extraction time by 15.1s compared to before optimization. Compared with other algorithms, the accuracy of extracting image style information based on a convolutional neural network was the highest at 80% under different iteration times. The research algorithm has improved the accuracy and time of extracting image style information.

@article{Xu2024,
  title     = {Optimization of DL Technology for Auxiliary Painting System Construction Based on FST Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Pengpeng Xu and Guo Chen},
  doi       = {10.14569/IJACSA.2024.0151192},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151192}
}